Selective presentation of rich experiences in search

ABSTRACT

A method of selectively providing different types of search engine results to different searchers is provided. A browsing history for each of a plurality of unique identities is logged. A unique identity is associated with a rich segment experience responsive to the browsing history for the unique identity satisfying correlation criteria of the rich segment experience. The rich segment experience is configured to present curated segment-specific information with other search results on a search result web page. Responsive to receiving a search query from the unique identity previously associated with the rich segment experience, the rich segment experience is presented with other search results on the search result web page. Responsive to receiving the search query from a different unique identity not previously associated with the rich segment experience, other search results are presented without the rich segment experience on the search result web page.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/353,308, filed on Mar. 14, 2019, and entitled “SELECTIVE PRESENTATIONOF RICH EXPERIENCES IN SEARCH”. The entirety of this application isincorporated herein by reference.

BACKGROUND

Internet search engines and other search providers may be designed toprovide many different search results in response to search queries.Internet search engines may be configured to populate a search resultweb page with different types of search results. For example, aconventional search result web page may include a list of uniformresource locators (URLs) for relevant web sites. Such URLs do notthemselves provide information relative to the search, but merely linkto web pages likely to have relevant information. Some search enginesmay also be configured to present, along with the other URLs, a richsegment experience that is itself a summary of useful informationrelevant to the search and not just a link to other information sources.A rich segment experience may include information related to a searchquery that is curated and presented in a visually distinct mannerrelative to other conventional URL search results. Rich segmentexperiences may make it easier for a user to quickly digest relevantinformation related to a search query without having to explore otherURLs.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter. Furthermore,the claimed subject matter is not limited to implementations that solveany or all disadvantages noted in any part of this disclosure.

A method of selectively providing different types of search engineresults to different searchers is provided. A browsing history for eachof a plurality of unique identities is logged. A unique identity isassociated with a rich segment experience responsive to the browsinghistory for the unique identity satisfying correlation criteria of therich segment experience. The rich segment experience is configured topresent curated segment-specific information with other search resultson a search result web page. Responsive to receiving a search query fromthe unique identity previously associated with the rich segmentexperience, the rich segment experience is presented with other searchresults on the search result web page. Responsive to receiving thesearch query from a different unique identity not previously associatedwith the rich segment experience, other search results are presentedwithout the rich segment experience on the search result web page.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically shows an example search engine computing system.

FIG. 2 is a flowchart depicting aspects of an example method for loggingbrowsing history information for a unique identity.

FIG. 3 is a flowchart depicting aspects of an example method forselectively providing different types of search engine results todifferent searchers.

FIGS. 4A-4F show example search result web pages with different searchresults that selectively include rich segment experiences based on thebrowsing history of different unique identities.

FIG. 5 schematically shows an example computing system.

DETAILED DESCRIPTION

In some cases, different searchers may perform the same search querywith different intentions or different interpretations of the searchquery. Prior to the herein disclosed search strategies, the differentsearchers may be provided with search results that include the same richsegment experience that the search engine correlates with the searchquery. For example, different searchers may provide the search query“Rams” to a search engine. A first searcher may be researching the LosAngeles Rams football team. A second searcher may be researching DodgeRam trucks. A third searcher may be researching male sheep. Depending onthe particular rich segment experience that the search engine correlatesto the search query, at least some of the different searchers will notfind the search results to be useful if all searchers are provided thesame rich segment experience.

Accordingly, the present disclosure is directed to a methodology fordetermining a searcher's interests towards a particularexperience/segment and using these interests as a factor to decidewhether or not to display a particular rich segment experienceresponsive to a search query. By selectively providing rich segmentexperiences based on a searcher's interests, the intentions of thesearcher may be anticipated and a more useful search result that istailored for the particular searcher may be provided.

FIG. 1 shows an example computing architecture that enables differenttypes of search engine results to be provided to different searchers. Asearch engine computing system 100 may be configured to provide searchfunctionality to a plurality of client computers 102 (e.g., 102A, 102B,102C). For example, the search engine computing system 100 may enable aclient computer 102 to perform searches using any suitable queries(e.g., natural language keywords, regular expression patterns, Booleanoperators for composing multiple queries, etc.), on any suitable searchdomain (e.g., web site(s), encyclopedia(s), database(s), socialnetworking platforms, financial/medical/scientific records, etc.). Thesearch engine computing system 100 may be communicatively coupled toeach of the plurality of client computers 102 via a computer network104, such as the Internet. The search engine computing system 100 may beconfigured to provide search functionality to any suitable number ofdifferent client computers 102.

The plurality of client computers 102 may be any suitable type ofcomputing device, e.g., a mobile phone, personal computer, intelligentassistant speaker device, etc. One or more of the plurality of clientcomputers 102 optionally may include a display subsystem configured topresent a web browser 106. The web browser 106 may be configured topresent one or more pages, e.g., a search page configured to allow auser to input search queries and/or view search results, examples ofwhich are shown in FIGS. 4A-4F described in further detail below.Alternately or additionally, one or more of the plurality of clientcomputers 102 may allow a user to input search queries and/or receivesearch results in any other suitable fashion, e.g., one or more of theplurality of client computers 102 may include a microphone and speakerand may be configured to receive queries via natural language speechutterances received at the microphone, and to output search results viaspeech audio output at the speaker. Although the present disclosure maybe described in terms of a graphical web browser 106, the methods andprocesses disclosed herein may be implemented in the context of anyother search interface, e.g., a speech-based natural language searchinterface.

Different client computers 102 may be associated with different uniqueidentities 108. The unique identities 108 may take any suitable form. Insome examples, a unique identity 108 may be a user identity that ispersistent across different computing devices. For example, a user mayperform search queries on a desktop computer, a tablet, and a smartphone that are all associated with the same unique identity, and all ofthose search queries may be attributed to that same unique identity. Insome such examples, the unique identities may take the form of anonymousidentifiers (ANIDs) anonymously linked to user accounts. In otherexamples, a unique identity 108 may be associated with a particularclient computer. For example, search behavior and/or browsing history ofdifferent users that share the same client computer 102 may be trackedvia the same unique identity 108 that is associated with the clientcomputer 102. In some such examples, the unique identities may take theform of client computing device identifiers (Client IDs).

In the depicted example, client computer 102A and client computer 102Bare associated with unique identity A, such that the search behavior andbrowsing history tracked on each of the client computers 102A and 102Bis attributed to unique identity A. For example, both client computers102A and 102B may be associated with the same user. Further, clientcomputer 102C is associated with unique identity B, such that the searchbehavior and browsing history tracked on client computer 102C isattributed to unique identity B, which differs from unique identity A.

The search engine computing system 100 is configured, responsive toreceiving a search query from a client computer 102, to return one ormore search results on a search result web page for presentation in theweb browser 106. The search engine computing system 100 may beconfigured to serve “raw” queries in the form of literal text input bythe user. Alternately or additionally, the search engine computingsystem 100 may be configured to serve “normalized” queries in the formof a computer-readable description of query content, e.g., by processinga computer-readable description indicating an intent of a queryrepresenting a question, goal, and/or task of the user indicated by thequery, by processing one or more entities in the query, and/or byprocessing syntactic structure of a query (e.g., a parse tree for thequery). Query normalization may be performed by any suitable computerdevice(s), e.g., by a client computer 102 and/or the search enginecomputing system 100. Normalized queries may include relevantinformational content of a query (e.g., relevant intents/entities) whilelimiting the amount of variability among queries (e.g., different rawqueries that are rephrasings of the same question may be normalized intothe same normalized query).

The search results may include different types of search results thatare retrieved from different sources. The search engine computing system100 may be configured to return results from any suitable domain, suchas different web sites on the internet or other domains. The searchengine computing system 100 may return search results based on datareceived from one or more other computers. For example, the searchengine computing system 100 may cooperate with a search results provider110 to send a search query to the search results provider 110 andreceive search results for a search query, which may include relevantweb sites, documents, etc., as desired for the search domain. The searchresults provider 110 may be configured to determine relevant searchresults for a query in any suitable fashion, e.g., by searching in adatabase, indexing/scraping web sites or documents, etc.

Non-limiting examples of search result entries that may be presented onthe search result web page may include non-curated search results, suchas a listing of URLs of relevant web sites. Such URLs do not themselvesprovide information relative to the search, but merely link to web pageslikely to have relevant information. Alternately or in addition to thenon-curated search results, the search result web page may selectivelyinclude rich segment experiences 132. Each rich segment experience 132may present a summary of useful information relevant to the search queryand not just a link to other information sources. Unlike the list ofURLs, the rich segment experiences are curated to provide enoughinformation that a user may not have to explore past the search resultpage—as opposed to clicking URLs to navigate to third party web sites.

A rich segment experience may include information related to a searchquery that is curated and presented in a visually distinct mannerrelative to other non-curated URL search results. In some examples, arich segment experience 132 may include graphics, images, animations,and/or videos. A rich segment experience 132 may have visualcharacteristics that draw attention to the rich segment experience. Forexample, a rich segment experience 132 may be positioned prominently onthe search result web page above other non-curated search results. Asanother example, a rich segment experience 132 may be positioned on aside panel of the search result web page. In some examples, a richsegment experience 132 may be formatted differently than othernon-curated search results. For example, a rich segment experience 132may be contained within a box or may be segregated from other searchresults by some form of border.

The search engine computing system 100 may be configured to maintain aplurality of rich segment experiences 132 in an experience database 130.Each different rich segment experience 132 may relate to a differenttopic, theme, subject, or other type of information. The experiencedatabase 130 may be configured to store any suitable number of differentrich segment experiences 132.

The search engine computing system 100 may include a ranker 112configured to rank a relevance of each search result entry relative tothe search query, whether it is a rich segment experience or anon-curated search result, such as a URL to a web site. The ranker 112may assign different rankings to different search result entries, andthe different search result entries may be presented on the searchresult web page based on the ranking. The ranker 112 may be configuredto rank the different search result entries according to any suitableranking technique. In some implementations, the ranker 112 may beconfigured to rank the rich segment experiences 132 separately from theother search results.

In one example, the ranker 112 may be configured to selectively presentdifferent rich segment experiences 132 on the search result web pagebased on a triggering algorithm 140. The ranker 112 may use thetriggering algorithm 140 to determine how relevant different richsegment experiences 132 stored in the experience database 130 are to aparticular search query. The triggering algorithm 140 may include one ormore thresholds for triggering presentation of a rich segment experience132. Each rich segment experience 132 may provide, to the ranker 112, aconfidence score 134 that is determined based on the search query.

Confidence scores may be generated in any suitable manner, e.g., usingany suitable combination of state-of-the-art and/or future machinelearning (ML), artificial intelligence (AI), and/or natural languageprocessing (NLP) techniques. For example, confidence scores may begenerated by an AI, ML, and/or NLP model based on input data includingany suitable combination of the search query, personalization data for auser, and/or data related to candidate rich segment experiences to beprovided for the search query. In some examples, the model may betrained to generate confidence scores with regard to a classificationtask, e.g., for classifying relevance, predicting user satisfaction,and/or correctly generating relevant rich segment experiences ascompared to “ground truth” examples of relevant rich segmentexperiences. For example, the model may be given input data andconfigured to output a relevance score, user satisfaction score, and/orselect a rich segment experience. The output of the model may beassessed according to a loss function (e.g., a loss function measuringaccuracy of scoring and/or selection). The model may be configured tooutput one or more confidence values for the classification task (e.g.,confidence values for different classification results regarding usersatisfaction and/or relevance, confidence values for the relevance ofdifferent candidate rich segment experiences, etc.). Similarly, insteador in addition to using a loss function with regard to a classificationtask, the model may be trained via reinforcement learning with regard toreinforcement signals, e.g., with regard to user satisfaction oruser-provided feedback regarding relevance of selected rich segmentexperiences. In either case, whether loss function or reinforcementlearning is used, the system may be adjusted over time to “penalize”incorrect actions by adjusting parameters of the system so that theincorrect actions are less likely in the future given similar inputs,and to “reward” correct actions by adjusting parameters of the system sothat the correct actions are more likely in the future. The lossfunction and/or reinforcement may be configured to more heavily rewardand/or penalize answers in proportion to the confidence value.Accordingly, the confidence value may be adjusted based on the lossfunction, e.g., to penalize confident but incorrect answers, and/or toreward confident, correct answers. Accordingly, the system may betrained to emit a relatively high confidence value when it is likely tobe correct, and to emit a relatively low confidence value when a correctanswer cannot be confidently predicted.

In some examples, different rich segment experiences may determineconfidence scores using different scoring techniques. The ranker 112 maybe configured to compare the confidence score 134 to the threshold, andif the confidence score 134 is greater than the threshold, the richsegment experience 132 may be presented. On the other hand, if theconfidence score is less than the threshold, then presentation of therich segment experience 132 may be suppressed.

In some implementations, the ranker 112 optionally may be configured toposition a rich segment experience on the search result web page basedon the confidence score 134. As an example, a rich segment having ahigher confidence score may be place more prominently on the searchresult page (e.g., at the top of the page above other search results).As another example, a rich segment having a medium confidence score thatis just high enough to exceed the threshold for presenting the richsegment experience may be placed less prominently on the search resultweb page (e.g., lower down on the page or on a side of the page).

Furthermore, in some examples where multiple rich segment experiences132 are triggered, the ranker 112 may be configured to select atriggered rich segment experience having the highest confidence score134 for presentation (or more prominent presentation). In some suchexamples, multiple rich segment experiences 132 may be presented on thesearch result web page. Further, in some examples, different portions ofthe search result web page may have different priority levels, such thatthe rich segment experience having the highest confidence score may bepositioned in the highest priority position (e.g., at the top of thepage) on the search result web page. The ranker 112 may traverse thelist to position the different rich segment experiences at the differentpriority positions on the search result web page based on the confidencescores. A rich segment experience may be positioned on the search resultweb page in any suitable position based on the confidence score of therich segment experience.

As discussed above, a searcher's interests may be used as a factor todecide whether or not to display a particular rich segment experienceresponsive to a search query. In some examples, the thresholds that areused by the triggering algorithm 140 to determine whether or not a richsegment experience is to be presented may be dependent upon whether ornot a searcher previously has shown interest in, and has been associatedwith the rich segment experience. In some such examples, the ranker 112may be configured to use a relaxed-trigger threshold if a uniqueidentity 108 that provided the search query is associated with a richsegment experience. Further, the ranker 112 may be configured to use astringent-trigger threshold that is greater than the relaxed-triggerthreshold, if the unique identity 108 is not associated with the richsegment experience. For example, because a search query for “Ninja” isquite broad, a non-personalized, stringent-trigger threshold fortriggering an “eSports” rich segment experience may be 0.8 or 80%.However, if the unique identity that provided the search query ispreviously associated with the “eSports” rich segment experience, then arelaxed-trigger threshold of 0.6 or 60% may trigger the “eSports” richsegment experience. The stringent-trigger threshold and therelaxed-trigger threshold may be set to any suitable thresholds for anysuitable rich segment experiences. In some examples, trigger thresholdsmay be determined based on the search query. For example, broader searchqueries may have lower thresholds and narrower search queries may havehigher thresholds.

Additionally or alternatively, in some examples, the confidence score134 provided by a rich segment experience 132 may be increased ordecreased based on whether or not a unique identity 108 is associatedwith the rich segment experience 132. For example, a confidence scorefor a rich segment experience may be increased based on a uniqueidentity being associated with the rich segment experience. By usingthese different thresholds and/or varying confidence scores based on asearcher's interest in/association with a rich segment experience, therich segment experience may be more likely to be triggered forpresentation on the search result web page relative to other richsegment experiences in which the searcher has not previously showninterest.

A rich segment experience 132 may be associated with one or more uniqueidentities 138 based on correlation criteria 136. In some examples,different rich segment experiences 132 may have different correlationcriteria 136. Any suitable correlation criteria 136 may be used by arich segment experience 132 to determine whether or not a uniqueidentity 108 has shown interest in, and thus should be associated withthe rich segment experience 132. For example, correlation criteria 136may be based on natural language processing, machine learning,artificial intelligence, data mining, according to direct one-to-onematching, “fuzzy” matching (e.g., matching with at least a thresholdsimilarity), and/or probabilistic matching. When a unique identity 108satisfies correlation criteria 136 for a rich segment experience 132,the unique identity 108 may be associated with the rich segmentexperience 132 in the experience database 130. As a natural extension ofthis example, a cluster or group of associated unique identities 138 maybe formed for each rich segment experience 132 and stored with the richsegment experience 132 in the experience database 130.

In one nonlimiting example, each rich segment experience 132 may bedefined in terms of a set of descriptive keyword tags. Further, eachunique identity 108 may have a set of descriptive keyword tags that isassociated with the unique identity based on previous search behaviorand/or browsing history 126. Each rich segment experience 132 maycompare its set of keyword tags with keyword tags of each uniqueidentity. When the set of keyword tags for a rich segment experience 132matches (or matches within at least a threshold similarity) keyword tagsassociated with a unique identity 108, the correlation criteria 136 ofthe rich segment experience 132 may be satisfied and the unique identity108 may be associated with the rich segment experience 132 in theexperience database 130.

The search engine computing system 100 may include a searchbehavior/browsing history logging pipeline 114 that may track the searchbehavior/browsing history of each unique identity 108 to facilitatedetermining correlation between unique identities and rich segmentexperiences. The logging pipeline 114 may include a unique identityextractor 116 configured to identify a unique identity 108 that providesa search query from a client computer 102 to the search engine computingsystem 100. The logging pipeline 114 may include a search query tagger118 configured to extract keywords from a search query. The search querytagger 118 may perform natural language processing (NLP) techniques onthe search query, for example, a part of speech (POS) tagger may be usedto derive keyword tags from the search query.

The logging pipeline 114 may include a search results tagger 120configured to derive keywords tags from search behavior/browsinghistory. In particular, the search results tagger 120 may be configuredto track which search results are selected from a search result web page(e.g., identify which URLs are clicked). Further, the search resultstagger 120 may be configured to perform NLP techniques on the selectedsearch results. For example, a text snippet may be extracted from a URLand a POS tagger may be used to derive keyword tags from the textsnippet. The search results tagger 120 may be configured to derivekeywords from rich segment experiences that are presented on the searchresult web page. The search results tagger 120 may be configured toderive keywords from search results on other granular search verticalssuch as news search results, image search results, video search results,shopping search results, recipe search results, etc.

The logging pipeline 114 optionally may include a search results filter122 that may be configured to remove designated search results frombeing used to generate keyword tags for the unique identity 108. In someimplementations, the search results filter 122 may be configured tofilter out selected search results that are considered non-positive(e.g., that lead to abandonment) from being used to generate keywordtags for the unique identity. For example, the search results filter 122may be configured to filter out selected search results where the dwelltime is less than a threshold time. As an example, a searcher may clickon a URL that causes a web site to be presented in the web browser, andthe searcher may then quickly navigate back to the search result webpage to select a different search result. The search results filter 122may filter out the selected search result in this case. In someexamples, the search results filter 122 may be configured to filter outother designated search results that may be deemed private or unwanted(as designated by the searcher or the computing system). As an example,the search results filter 122 may be configured to filter out searchresults relating to adult content. As another example, the searchresults filter 122 may be configured to filter out search resultsrelating to private information (e.g., medical information).

The keyword tags and the other search behavior/browsing historyinformation 126 tracked by the logging pipeline 114 for each uniqueidentity 108 may be maintained in an identity database 124. The identitydatabase 124 may be configured to store any suitable number of uniqueidentities 108 and associated browsing history 126. The identitydatabase 124 may store any suitable information relating to the uniqueidentities 108. In some examples, each unique identity 108 may beassociated with a different set of keyword tags aggregated for theunique identity 108 based on search behavior/browsing history in theidentity database 124.

As discussed above, the set of keyword tags associated with each richsegment experience 132 may be compared to the keyword tags associatedwith each of the unique identities 108 to form different clusters ofunique identities 138 that are interested in the different rich segmentexperiences 132. Each cluster of unique identities 138 may be associateda rich segment experience in the experience database 130. Alternativelyor additionally, a different cluster of rich segment experiences 128 maybe associated with each unique identity 108 in the identity datastore124. In either case, the associated information may be provided to theranker 112, and the ranker 112 may tune the triggering algorithm140—e.g., by using relaxed/stringent triggering threshold and/oradjusting confidence scores based on such information. By tuning thetriggering algorithm 140 in this manner, rich segment experiences forwhich a searcher has previously expressed interest may be more likely tobe presented on the search result web page relative to other richsegment experiences for which the search has not previously expressedinterest. In this way, the search results may be more likely to beuseful and in line with the expectations of the searcher.

FIG. 2 depicts aspects of an example method 200 for logging browsinghistory information for a unique identity. The method 200 may beperformed by any suitable computer system, e.g., by search enginecomputing system 100 and/or client computers 102. For example, thesearch engine computing system 100 may instantiate the logging pipeline114 to perform the method 200.

At 202, the method 200 includes receiving a search query from a clientcomputer. At 204, the method 202 includes identifying a unique identitythat provided the search query. In some examples, the unique identitymay be an AND that is associated with a searcher and that may be usedacross multiple different client computers. In some examples, the uniqueidentity may be a Client ID associated with a particular clientcomputer.

At 206, the method 200 includes generating keyword tags derived from thesearch query. Such keyword tags may be included in a set of keyword tagsassociated with the unique identity that characterize the interests ofthe unique identity.

At 208, the method 200 includes providing search results based on thesearch query on a search result web page presented in a web browser ofthe client computer.

At 210, the method 200 includes generating keyword tags derived fromsearch results. In some implementations, at 212, the method optionallymay include generating keyword tags derived from selected searchresults. As an example, the keyword tags may be derived from textsnippets included in a selected URL. As another example, the keywordtags may be derived from information on a web site selected from thesearch result web page. In some implementations, at 214, the method 200optionally may include generating keyword tags derived from rich segmentexperiences presented on the search result web page. In someimplementations, at 216, the method 200 optionally may includegenerating keyword tags derived from other vertical search results(e.g., image search, video search, person search, recipe search). Suchkeyword tags may be included in the set of keyword tags associated withthe unique identity that characterize the interests of the uniqueidentity.

At 218, the method 200 optionally may include filtering designatedsearch results from being used to generate the keyword tags. As anexample, selected search results that are considered non-positive (e.g.,that lead to abandonment) may be filtered from being used to generatekeyword tags for the unique identity. Other non-limiting examplesinclude filtering search results relating to adult content and privateinformation.

At 220, the method 200 includes associating the unique identity with oneor more rich segment experiences based on correlation criteria. As anexample, if a set of keyword tags associated with a rich segmentexperience at least partially matches keyword tags associated with theunique identity, then the correlation criteria is satisfied and theunique identify is associated with the rich segment experience.

The method 200 may be performed for each of a plurality of uniqueidentities to determine the interests of each unique identity and therich segment experiences associated with each unique identity. Suchinformation may be used to provide different types of search engineresults to different searchers as discussed herein.

FIG. 3. depicts aspects of an example method 300 for selectivelyproviding different types of search engine results to differentsearchers. The method 300 may be performed by any suitable computersystem, e.g., by search engine computing system 100 and/or clientcomputers 102.

At 302, the method 300 includes receiving a search query from a uniqueidentity. The method 300 may perform in parallel a series of methodsteps for each of a plurality of rich segment experiences to determinewhether or not the rich segment experience should be presented on asearch result web page based on the search query. For each rich segmentexperience, at 304, the method 300 includes determining whether theunique identity is associated with the rich segment experience. If theunique identity is associated with the rich segment experience, then themethod 300 moves to 306. Otherwise, the unique identity is notassociated with the rich segment experience, and the method 300 moves to308.

At 306, the method 300 includes determining whether the rich segmentexperience is greater than a relaxed-trigger threshold. For example, therich segment experience may generate a confidence score based on thesearch query and the confidence score may be compared to therelaxed-trigger threshold. If the rich segment experience is greaterthan the relaxed-trigger threshold, the method 300 moves to 312.Otherwise, the method 300 moves to 310.

At 308, the method 300 includes determining whether the rich segmentexperience is greater than a stringent-trigger threshold that is greaterthan the relaxed-trigger threshold. For example, the rich segmentexperience may generate a confidence score based on the search query andthe confidence score may be compared to the stringent-trigger threshold.If the rich segment experience is greater than the stringent-triggerthreshold, the method 300 moves to 312. Otherwise, the method 300 movesto 310.

At 310, the method 300 includes presenting other search results withoutpresenting the rich segment experience on a search result web page. Forexample, the other search results may include URLs to relevant web sitesand/or a different, more relevant rich segment experience.

At 312, the method 300 includes presenting the rich segment experiencewith other search results on the search result web page. In someexamples, the rich segment experience optionally may be presented on thesearch result web page without any non-curated search results (e.g., URLlinks) or any other search results.

In some implementations, at 314, the method 300 optionally may includepositioning the rich segment experience on the search result web pagebased on a confidence score for the rich segment experience. Forexample, the higher the confidence score for the rich segmentexperience, the higher on the search results web page the rich segmentexperience may be positioned.

Method steps 304-314 may be repeated for each of the plurality of richsegment experiences to determine whether or not each rich segmentexperience is to be presented on the search result web page.

FIGS. 4A-4F show an example search result web page with different searchresults that selectively include rich segment experiences based on thesearch behavior/browsing history of different unique identities. Inparticular, search results are shown for the same search queriesperformed by unique identity A and unique identity B. Unique identity Ahas an interest in the collectible card game, Magic the Gathering, whichhas been determined based on previous search behavior/browsing historyfor unique identity A. Unique identity B has an interest in the OrlandoMagic basketball team, which has been determined based on previoussearch behavior/browsing history for unique identity B.

In FIG. 4A, a search results web page 400 includes a search query 402for “MAGIC THE GATHERING” performed by unique identity A. The searchquery 402 may be provided as input to a triggering algorithm. Each of aplurality of different rich segment experiences may determine aconfidence score based on the search query 402. The triggering algorithmmay be configured to compare the plurality of confidence scores todifferent trigger thresholds. In particular, confidence scores for richsegment experiences that are associated with unique identity A may becompared to a relaxed-trigger threshold, and confidence scores for richsegment experiences that are not associated with unique identity A maybe compared to a stringent-trigger threshold.

A rich segment experience 404 for “MAGIC: THE GATHERING” is associatedwith unique identity A, because unique identity A is interested in thistype of collectible card game experience. As such, a confidence scorefor the rich segment experience 404 is compared to the relaxed-triggerthreshold. The confidence score for the rich segment experience 404exceeds the relaxed-trigger threshold, which triggers the rich segmentexperience 404 to be presented on the search result web page 400 forunique identity A.

The rich segment experience 404 includes images of different cards aswell as other curated information relating to rules of the game,articles about the game, tournaments to play the game, cards for sale,and downloading a digital version of the game. This curated informationmay be retrieved from different web sites and presented in the richsegment experience 404. Additionally, other non-curated search results406 are presented on the search result web page 400 based on the searchquery 402 performed by unique identity A. The other non-curated searchresults 406 include URLs to different relevant web sites. In someexamples, the other non-curated search results 406 may be personalizedfor unique identity A. In other examples, the other non-curated searchresults 406 may be non-personalized, such that the same search resultsare presented to different unique identities based on the search query402.

Furthermore, a different rich segment experience 412 for “ORLANDO MAGICBASKETBALL TEAM” (shown in FIG. 4D) is not associated with uniqueidentity A, because unique identity A has not previously demonstratedinterest (e.g., via prior search and/or browsing activity) in thisbasketball team. As such, a confidence score for the rich segmentexperience 412 is compared to the stringent-trigger threshold. Theconfidence score for the rich segment experience 412 does not exceed thestringent-trigger threshold and thus is not presented on the searchresult web page 400 for unique identity A based on the search query 402.

In FIG. 4B, the search results web page 400 includes the same searchquery 402 for “MAGIC THE GATHERING” that was performed by uniqueidentity A in FIG. 4A, but instead is performed by unique identity B.The rich segment experience 404 for “MAGIC: THE GATHERING” is notassociated with unique identity B, because unique identity B has notpreviously demonstrated interest (e.g., via prior search and/or browsingactivity) in this type of collectible card game experience. As such, theconfidence score for the rich segment experience 404 is compared to thestringent-trigger threshold. The confidence score for the rich segmentexperience 404 exceeds the stringent-trigger threshold, which triggersthe rich segment experience 404 to be presented on the search result webpage 400 for unique identity B. Additionally, other non-curated searchresults 408 are presented on the search result web page 400 based on thesearch query 402 performed by unique identity B.

Furthermore, the rich segment experience 412 for “ORLANDO MAGICBASKETBALL TEAM” (shown in FIG. 4D) is associated with unique identityB, because unique identity B is interested in this basketball team. Assuch, the confidence score for the rich segment experience 412 iscompared to the relaxed-trigger threshold. The confidence score for therich segment experience 412 does not exceed the relaxed-triggerthreshold and thus is not presented on the search result web page 400for unique identity B based on the search query 402.

Note that FIG. 4B shows that even if a unique identity is previouslyassociated with the rich segment experience 412 for “ORLANDO MAGICBASKETBALL TEAM”, the unique identity may not be provided that richsegment experience if the search query is sufficiently specific towardsanother rich segment experience (e.g., the rich segment experience 404).Further note that FIGS. 4A and 4B collectively show that the same richsegment experience may be provided to different unique identities havingdifferent interests that enter the same search query if that richsegment experience is sufficiently specific to that search query.

In FIG. 4C, the search results web page 400 includes a search query 408for “MAGIC ORLANDO” performed by unique identity A. The confidence scorefor the rich segment experience 404 for “MAGIC: THE GATHERING” iscompared to the relaxed-trigger threshold, because the rich segmentexperience 404 is associated with unique identity A. The confidencescore for the rich segment experience 404 exceeds the relaxed-triggerthreshold, which triggers the rich segment experience 404 to bepresented on the search result web page 400 for unique identity A.Additionally, other non-curated search results 410 are presented on thesearch result web page 400 based on the search query 408 performed byunique identity A. The other search results 410 include URLs todifferent relevant web sites for the search query 408 includingtournaments to play the card game in Orlando and shops that sell thecard game in Orlando.

Furthermore, the confidence score for the rich segment experience 412for “ORLANDO MAGIC BASKETBALL TEAM” (shown in FIG. 4D) is compared tothe stringent-trigger threshold, because the rich segment experience 412is not associated with unique identity A. The confidence score for therich segment experience 412 does not exceed the stringent-triggerthreshold and thus is not presented on the search result web page 400for unique identity A based on the search query 408.

In FIG. 4D, the search results web page 400 includes the search query408 for “MAGIC ORLANDO” performed by unique identity B. The confidencescore for the rich segment experience 412 for “ORLANDO MAGIC BASKETBALLTEAM” is compared to the relaxed-trigger threshold, because the richsegment experience 412 is associated with identity B. The rich segmentexperience 412 has a confidence score that exceeds the relaxed-triggerthreshold, which triggers the rich segment experience 412 to bepresented on the search result web page 400 for unique identity B.

The rich segment experience 412 includes the score of the current gamebeing played by the Orlando Magic basketball team, as well as othercurated information relating to the roster, schedule, standings,statistics and tickets for the basketball team. This curated informationmay be retrieved from different web sites and presented in the richsegment experience 412. Additionally, other non-curated search results414 are presented on the search result web page 400 based on the searchquery 408 performed by unique identity B. The other non-curated searchresults 414 include URLs to different relevant web sites for the searchquery 416.

Furthermore, the rich segment experience 404 for “MAGIC: THE GATHERING”(shown in FIG. 4C) is compared to the stringent-trigger threshold,because the rich segment experience 404 is not associated with uniqueidentity B. The confidence score for the rich segment experience 404does not exceed the stringent-trigger threshold and thus is notpresented on the search result web page 400 for unique identity B basedon the search query 408.

Note that FIGS. 4C and 4D collectively show that different rich segmentexperiences may be provided to different unique identities even if thedifferent unique identities enter the same search query based on thoseunique identities' prior associations with the different rich segmentexperiences.

In FIG. 4E, the search results web page 400 includes a search query 416for “ORLANDO MAGIC BASKETBALL” performed by unique identity A. Theconfidence score for the rich segment experience 412 for “ORLANDO MAGICBASKETBALL TEAM” is compared to the stringent-trigger threshold, becausethe rich segment experience 412 is not associated with unique identityA. The confidence score for the rich segment experience 412 exceeds thestringent-trigger threshold, which triggers the rich segment experience412 to be presented on the search result web page 400 for uniqueidentity A. Additionally, other non-curated search results 414 arepresented on the search result web page 400 based on the search query416 performed by unique identity A. The other non-curated search results414 include URLs to different relevant web sites based on the searchquery 416.

Furthermore, the rich segment experience 404 for “MAGIC: THE GATHERING”(shown in FIG. 4C) is compared to the relaxed-trigger threshold, becausethe rich segment experience 404 is associated with unique identity A.The confidence score for the rich segment experience 404 does not exceedthe relaxed-trigger threshold and thus is not presented on the searchresult web page 400 for unique identity A based on the search query 416.

Note that FIG. 4E shows that even if a unique identity is previouslyassociated with the rich segment experience 404 for “MAGIC: THEGATHERING”, the unique identity may not be provided that rich segmentexperience if the search query is sufficiently specific towards anotherrich segment experience (e.g., the rich segment experience 412).

In FIG. 4F, the search results web page 400 includes the search query416 for “ORLANDO MAGIC BASKETBALL” performed by unique identity B. Theconfidence score for the rich segment experience 412 for “ORLAND MAGICBASKETBALL TEAM” is compared to the relaxed-trigger threshold, becausethe rich segment experience 412 is associated with unique identity B.The confidence score for the rich segment experience 412 exceeds therelaxed-trigger threshold, which triggers the rich segment experience412 to be presented on the search result web page 400 for uniqueidentity B. Additionally, other non-curated search results 414 arepresented on the search result web page 400 based on the search query416 performed by unique identity B.

Furthermore, the rich segment experience 404 for “MAGIC: THE GATHERING”(shown in FIG. 4C) is compared to the stringent-trigger threshold,because the rich segment experience 404 is not associated with uniqueidentity B. The confidence score for the rich segment experience 404does not exceed the stringent-trigger threshold and thus is notpresented on the search result web page 400 for unique identity B basedon the search query 416.

Note that FIGS. 4E and 4F collectively show that the same rich segmentexperience may be provided to different unique identities havingdifferent interests that enter the same search query if that richsegment experience is sufficiently specific to that search query.

Although the concepts described herein are directed to presentingdifferent search results to different searchers on a search result webpage, it will be appreciated that the different search results may takeany other suitable form. For example, a rich segment experience may be aNatural Language result that is spoken (e.g., by a personal assistantAlexa/Siri/Cortana).

The methods and processes described herein may be tied to a computingsystem of one or more computing devices. In particular, such methods andprocesses may be implemented as an executable computer-applicationprogram, a network-accessible computing service, anapplication-programming interface (API), a library, or a combination ofthe above and/or other compute resources.

FIG. 5 schematically shows a simplified representation of a computingsystem 500 configured to provide any to all of the compute functionalitydescribed herein. Computing system 500 may take the form of one or morepersonal computers, network-accessible server computers, tabletcomputers, home-entertainment computers, gaming devices, mobilecomputing devices, mobile communication devices (e.g., smart phone),virtual/augmented/mixed reality computing devices, wearable computingdevices, Internet of Things (IoT) devices, embedded computing devices,and/or other computing devices. For example, computing system 500 mayinclude any combination of logic subsystems, storage subsystems, and/orother subsystems of one or more of the search engine computing system100, client computers 102, service endpoint 120, search results provider110, identity datastore 124, and experience database 130.

Computing system 500 includes a logic subsystem 502 and a storagesubsystem 504. Computing system 500 may optionally include aninput/output subsystem 506 (e.g., comprising one or more input devicesor sensors, and one or more output devices such as a graphical displayand/or audio speakers), communication subsystem 508, and/or othersubsystems not shown in FIG. 5.

Logic subsystem 502 includes one or more physical devices configured toexecute instructions. For example, the logic subsystem may be configuredto execute instructions that are part of one or more applications,services, or other logical constructs. The logic subsystem may includeone or more hardware processors configured to execute softwareinstructions. Additionally or alternatively, the logic subsystem mayinclude one or more hardware or firmware devices configured to executehardware or firmware instructions. Processors of the logic subsystem maybe single-core or multi-core, and the instructions executed thereon maybe configured for sequential, parallel, and/or distributed processing.Individual components of the logic subsystem optionally may bedistributed among two or more separate devices, which may be remotelylocated and/or configured for coordinated processing. Aspects of thelogic subsystem may be virtualized and executed by remotely-accessible,networked computing devices configured in a cloud-computingconfiguration.

Storage subsystem 504 includes one or more physical devices configuredto temporarily and/or permanently hold computer information such as dataand instructions executable by the logic subsystem. When the storagesubsystem includes two or more devices, the devices may be collocatedand/or remotely located. Storage subsystem 504 may include volatile,nonvolatile, dynamic, static, read/write, read-only, random-access,sequential-access, location-addressable, file-addressable, and/orcontent-addressable devices. Storage subsystem 504 may include removableand/or built-in devices. When the logic subsystem executes instructions,the state of storage subsystem 504 may be transformed—e.g., to holddifferent data.

Aspects of logic subsystem 502 and storage subsystem 504 may beintegrated together into one or more hardware-logic components. Suchhardware-logic components may include program- and application-specificintegrated circuits (PASIC/ASICs), program- and application-specificstandard products (PSSP/ASSPs), system-on-a-chip (SOC), and complexprogrammable logic devices (CPLDs), for example.

The logic subsystem and the storage subsystem may cooperate toinstantiate one or more logic machines. As used herein, the term“machine” is used to collectively refer to hardware and any software,instructions, and/or other components cooperating with such hardware toprovide computer functionality. In other words, “machines” are neverabstract ideas and always have a tangible form. A machine may beinstantiated by a single computing device, or a machine may include twoor more sub-components instantiated by two or more different computingdevices. In some implementations a machine includes a local component(e.g., computer service) cooperating with a remote component (e.g.,cloud computing service). The software and/or other instructions thatgive a particular machine its functionality may optionally be saved asan unexecuted module on a suitable storage device. Non-limiting examplesof machines which may be instantiated by computing system 500 accordingto the present disclosure include web browser 106, search resultsprovider 110, ranker 112, and components of logging pipeline 114.

Machines according to the present disclosure may be implemented usingany suitable combination of state-of-the-art and/or future machinelearning (ML), artificial intelligence (AI), and/or natural languageprocessing (NLP) techniques. Non-limiting examples of techniques thatmay be incorporated in an implementation of one or more machines includesupport vector machines, multi-layer neural networks, convolutionalneural networks (e.g., including spatial convolutional networks forprocessing images and/or videos, temporal convolutional neural networksfor processing audio signals and/or natural language sentences, and/orany other suitable convolutional neural networks configured to convolveand pool features across one or more temporal and/or spatialdimensions), recurrent neural networks (e.g., long short-term memorynetworks), associative memories (e.g., lookup tables, hash tables, BloomFilters, Neural Turing Machine and/or Neural Random Access Memory), wordembedding models (e.g., GloVe or Word2Vec), unsupervised spatial and/orclustering methods (e.g., nearest neighbor algorithms, topological dataanalysis, and/or k-means clustering), graphical models (e.g., (hidden)Markov models, Markov random fields, (hidden) conditional random fields,and/or AI knowledge bases), and/or natural language processingtechniques (e.g., tokenization, stemming, constituency and/or dependencyparsing, and/or intent recognition, segmental models, and/orsuper-segmental models (e.g., hidden dynamic models)).

In some examples, the methods and processes described herein may beimplemented using one or more differentiable functions, wherein agradient of the differentiable functions may be calculated and/orestimated with regard to inputs and/or outputs of the differentiablefunctions (e.g., with regard to training data, and/or with regard to anobjective function). Such methods and processes may be at leastpartially determined by a set of trainable parameters. Accordingly, thetrainable parameters for a particular method or process may be adjustedthrough any suitable training procedure, in order to continually improvefunctioning of the method or process. For example, machine learningtraining techniques may be used to mine user approval/disapprovalsignals, e.g., to determine whether to add new query blacklist entries,site rules, and/or pattern rules for suppressing query answers.

Non-limiting examples of training procedures for adjusting trainableparameters include supervised training (e.g., using gradient descent orany other suitable optimization method), zero-shot, few-shot,unsupervised learning methods (e.g., classification based on classesderived from unsupervised clustering methods), reinforcement learning(e.g., deep Q learning based on feedback) and/or generative adversarialneural network training methods, belief propagation, RANSAC (randomsample consensus), contextual bandit methods, maximum likelihoodmethods, and/or expectation maximization. In some examples, a pluralityof methods, processes, and/or components of systems described herein maybe trained simultaneously with regard to an objective function measuringperformance of collective functioning of the plurality of components(e.g., with regard to reinforcement feedback and/or with regard tolabelled training data). Simultaneously training the plurality ofmethods, processes, and/or components may improve such collectivefunctioning. In some examples, one or more methods, processes, and/orcomponents may be trained independently of other components (e.g.,offline training on historical data).

The methods and processes disclosed herein may be configured to giveusers and/or any other humans control over any private and/orpotentially sensitive data. Whenever data is stored, accessed, and/orprocessed, the data may be handled in accordance with privacy and/orsecurity standards. When user data is collected, users or otherstakeholders may designate how the data is to be used and/or stored.Whenever user data is collected for any purpose, the user owning thedata should be notified, and the user data should only be collected whenthe user provides affirmative consent. If data is to be collected, itcan and should be collected with the utmost respect for user privacy. Ifthe data is to be released for access by anyone other than the user orused for any decision-making process, the user's consent may becollected before using and/or releasing the data. Users may opt-inand/or opt-out of data collection at any time. After data has beencollected, users may issue a command to delete the data, and/or restrictaccess to the data. All potentially sensitive data optionally may beencrypted and/or, when feasible anonymized, to further protect userprivacy. Users may designate portions of data, metadata, orstatistics/results of processing data for release to other parties,e.g., for further processing. Data that is private and/or confidentialmay be kept completely private, e.g., only decrypted temporarily forprocessing, or only decrypted for processing on a user device andotherwise stored in encrypted form. Users may hold and controlencryption keys for the encrypted data. Alternately or additionally,users may designate a trusted third party to hold and control encryptionkeys for the encrypted data, e.g., so as to provide access to the datato the user according to a suitable authentication protocol.

When the methods and processes described herein incorporate ML and/or AIcomponents, the ML and/or AI components may make decisions based atleast partially on training of the components with regard to trainingdata. Accordingly, the ML and/or AI components can and should be trainedon diverse, representative datasets that include sufficient relevantdata for diverse users and/or populations of users. In particular,training data sets should be inclusive with regard to different humanindividuals and groups, so that as ML and/or AI components are trained,performance is improved with regard to the user experience of the usersand/or populations of users.

For example, a dialogue system according to the present disclosure maybe trained to interact with different populations of users, usinglanguage models that are trained to work well for those populationsbased on language, dialect, accent, and/or any other features ofspeaking style of the population.

ML and/or AI components may additionally be trained to make decisions soas to minimize potential bias towards human individuals and/or groups.For example, when AI systems are used to assess any qualitative and/orquantitative information about human individuals or groups, they may betrained so as to be invariant to differences between the individuals orgroups that are not intended to be measured by the qualitative and/orquantitative assessment, e.g., so that any decisions are not influencedin an unintended fashion by differences among individuals and groups.

ML and/or AI components can and should be designed to provide context asto how they operate as much as is possible, so that implementers of MLand/or AI systems can be accountable for decisions/assessments made bythe systems. For example, ML and/or AI systems should have replicablebehavior, e.g., when they make pseudo-random decisions, random seedsshould be used and recorded to enable replicating the decisions later.As another example, data used for training and/or testing ML and/or AIsystems should be curated and maintained to facilitate futureinvestigation of the behavior of the ML and/or AI systems with regard tothe data. Furthermore, ML and/or AI systems can and should becontinually monitored to identify potential bias, errors, and/orunintended outcomes.

When included, input/output subsystem 506 may be used to present avisual representation of data held by storage subsystem 504. This visualrepresentation may take the form of a graphical user interface (GUI).Input/output subsystem 506 may include one or more display devicesutilizing virtually any type of technology. In some implementations,input/output subsystem 506 may include one or more virtual-, augmented-,or mixed reality displays. Input/output subsystem 506 may be used tovisually present content, such as browser 106 and search resultsdisplayed in pages of browser 106. Input/output subsystem 506 mayinclude one or more microphone and/or speaker devices configured toreceive and/or output audio. In some examples, microphone devices may beused to receive speech audio input which may be processed (e.g., usingnatural language processing and/or machine learning techniques) toreceive user queries, determine user intent, etc. For example, speechaudio input may be processed to control browser 106. For example, speechaudio input may be processed to recognize user queries for a searchengine, e.g., in addition or instead of user input via text in a searchbar. In some examples, speaker devices may be used to output speechaudio, e.g., to provide information to the user, interact with the userin spoken conversation, etc. In some examples, browser 106 may beconfigured to present content in the form of speech audio. For example,browser 106 may present search results by outputting, for each resultentry in the search results, speech audio indicating the result entry.For example, when browser 106 presents search results including a queryanswer and a plurality of other result entries, browser 106 may outputspeech audio reciting the query answer, and output further speech audiolisting a title and/or summary of each of the plurality of other resultentries.

When included, input/output subsystem may further comprise or interfacewith one or more input devices. An input device may include a sensordevice or a user input device. Examples of user input devices include akeyboard, mouse, touch screen, or game controller. In some embodiments,the input subsystem may comprise or interface with selected natural userinput (NUI) componentry. Such componentry may be integrated orperipheral, and the transduction and/or processing of input actions maybe handled on- or off-board. Example NUI componentry may include amicrophone for speech and/or voice recognition; an infrared, color,stereoscopic, and/or depth camera for machine vision and/or gesturerecognition; a head tracker, eye tracker, accelerometer, and/orgyroscope for motion detection and/or intent recognition.

When included, communication subsystem 508 may be configured tocommunicatively couple computing system 500 with one or more othercomputing devices. Communication subsystem 508 may include wired and/orwireless communication devices compatible with one or more differentcommunication protocols. The communication subsystem may be configuredfor communication via personal-, local- and/or wide-area networks.

Language models may utilize vocabulary features to guidesampling/searching for words for recognition of speech. For example, alanguage model may be at least partially defined by a statisticaldistribution of words or other vocabulary features. For example, alanguage model may be defined by a statistical distribution of n-grams,defining transition probabilities between candidate words according tovocabulary statistics. The language model may be further based on anyother appropriate statistical features, and/or results of processing thestatistical features with one or more machine learning and/orstatistical algorithms (e.g., confidence values resulting from suchprocessing). In some examples, a statistical model may constrain whatwords may be recognized for an audio signal, e.g., based on anassumption that words in the audio signal come from a particularvocabulary.

Alternately or additionally, the language model may be based on one ormore neural networks previously trained to represent audio inputs andwords in a shared latent space, e.g., a vector space learned by one ormore audio and/or word models (e.g., wav2letter and/or word2vec).Accordingly, finding a candidate word may include searching the sharedlatent space based on a vector encoded by the audio model for an audioinput, in order to find a candidate word vector for decoding with theword model. The shared latent space may be utilized to assess, for oneor more candidate words, a confidence that the candidate word isfeatured in the speech audio.

The language model may be used in conjunction with an acoustical modelconfigured to assess, for a candidate word and an audio signal, aconfidence that the candidate word is included in speech audio in theaudio signal based on acoustical features of the word (e.g.,mel-frequency cepstral coefficients, formants, etc.). Optionally, insome examples, the language model may incorporate the acoustical model(e.g., assessment and/or training of the language model may be based onthe acoustical model). The acoustical model defines a mapping betweenacoustic signals and basic sound units such as phonemes, e.g., based onlabelled speech audio. The acoustical model may be based on any suitablecombination of state-of-the-art or future machine learning (ML) and/orartificial intelligence (AI) models, for example: deep neural networks(e.g., long short-term memory, temporal convolutional neural network,restricted Boltzmann machine, deep belief network), hidden Markov models(HMM), conditional random fields (CRF) and/or Markov random fields,Gaussian mixture models, and/or other graphical models (e.g., deepBayesian network). Audio signals to be processed with the acoustic modelmay be pre-processed in any suitable manner, e.g., encoding at anysuitable sampling rate, Fourier transform, band-pass filters, etc. Theacoustical model may be trained to recognize the mapping betweenacoustic signals and sound units based on training with labelled audiodata. For example, the acoustical model may be trained based on labelledaudio data comprising speech audio and corrected text, in order to learnthe mapping between the speech audio signals and sound units denoted bythe corrected text. Accordingly, the acoustical model may be continuallyimproved to improve its utility for correctly recognizing speech audio.

In some examples, in addition to statistical models, neural networks,and/or acoustical models, the language model may incorporate anysuitable graphical model, e.g., a hidden Markov model (HMM) or aconditional random field (CRF). The graphical model may utilizestatistical features (e.g., transition probabilities) and/or confidencevalues to determine a probability of recognizing a word, given thespeech audio and/or other words recognized so far. Accordingly, thegraphical model may utilize the statistical features, previously trainedmachine learning models, and/or acoustical models to define transitionprobabilities between states represented in the graphical model.

In an example, a method of selectively providing different types ofsearch engine results to different searchers comprises for each of aplurality of different unique identities, logging a browsing history forthe unique identity, for each of a plurality of different rich segmentexperiences configured to present curated segment-specific informationwith other search results on a search result web page, associating theunique identity with the rich segment experience responsive to thebrowsing history for the unique identity satisfying correlation criteriaof the rich segment experience, responsive to receiving a search queryfrom the unique identity previously associated with the rich segmentexperience, presenting the rich segment experience with other searchresults on the search result web page, and responsive to receiving thesearch query from a different unique identity not previously associatedwith the rich segment experience, presenting other search resultswithout the rich segment experience on the search result web page. Inthis example and/or other examples, the rich segment experience may bepresented with other search results only if the search query receivedfrom the unique identity exceeds a relaxed-trigger threshold. In thisexample and/or other examples, the method may further comprisepresenting the rich segment experience with other search results if thesearch query received from the different unique identity exceeds astringent-trigger threshold greater than the relaxed-trigger threshold.In this example and/or other examples, the rich segment experience maynot be presented with other search results if the search query receivedfrom the unique identity does not exceed a relaxed-trigger threshold. Inthis example and/or other examples, logging the browsing history for theunique identity may include generating keyword tags derived from one ormore of previous search queries, previously selected search results, andpreviously presented rich segment experiences. In this example and/orother examples, each rich segment experience may be defined in terms ofa different set of keyword tags, and the correlation criteria may besatisfied based on a set of keyword tags associated with the richsegment experience matching keyword tags associated with the uniqueidentity. In this example and/or other examples, logging the browsinghistory for the unique identity may include filtering out previouslyselected search results having dwell times that are less than athreshold time from being used to generate keyword tags for the uniqueidentity. In this example and/or other examples, the method may furthercomprise generating a confidence score for the rich segment experiencebased on the search query, and wherein the rich segment experience ispositioned on the search result web page based on the confidence score.In this example and/or other examples, the other search results may beindividualized for the unique identity based on the browsing history forthe unique identity. In this example and/or other examples, the uniqueidentity may be associated with a user identity across differentcomputing devices. In this example and/or other examples, the uniqueidentity may be associated with a particular computing device.

In an example, a method of selectively providing different types ofsearch engine results comprises logging a browsing history for a uniqueidentity, for each of a plurality of different rich segment experiencesconfigured to present curated segment-specific information with othersearch results on a search result web page, associating the uniqueidentity with the rich segment experience responsive to the browsinghistory for the unique identity satisfying correlation criteria of therich segment experience, in response to receiving a search query fromthe unique identity, presenting a rich segment experience with othersearch results on the search result web page based on the rich segmentexperience being previously associated with the unique identity and therich segment experience exceeding a relaxed-trigger threshold,presenting other search results on the search result web page withoutthe rich segment experience based on the rich segment experience beingpreviously associated with the unique identity and the rich segmentexperience not exceeding the relaxed-trigger threshold, presenting therich segment experience with other search results on the search resultweb page based on the rich segment experience not being previouslyassociated with the unique identity and the rich segment experienceexceeding a stringent-trigger threshold, and presenting other searchresults on the search result web page without the rich segmentexperience based on the rich segment experience not being previouslyassociated with the unique identity and the rich segment experience notexceeding the stringent-trigger threshold. In this example and/or otherexamples, logging the browsing history for the unique identity mayinclude generating keyword tags derived from one or more of previoussearch queries, previously selected search results, previously presentedrich segment experiences. In this example and/or other examples, eachrich segment experience may be defined in terms of a different set ofkeyword tags, and the correlation criteria may be satisfied based on aset of keyword tags associated with the rich segment experience matchingkeyword tags associated with the unique identity. In this example and/orother examples, logging the browsing history for the unique identity mayinclude filtering out previously selected search results having dwelltimes that are less than a threshold time from being used to generatekeyword tags for the unique identity. In this example and/or otherexamples, the method may further comprise generating a confidence scorefor the rich segment experience based on the search query, and the richsegment experience may be positioned on the search result web page basedon the confidence score.

In an example, a method of selectively providing different types ofsearch engine results to different searchers comprises for each of aplurality of different unique identities, logging a browsing history forthe unique identity, for each of a plurality of different rich segmentexperiences configured to present curated segment-specific informationwith other search results on a search result web page, associating theunique identity with the rich segment experience responsive to thebrowsing history for the unique identity satisfying correlation criteriaof the rich segment experience, responsive to receiving a search queryfrom the unique identity previously associated with the rich segmentexperience, presenting the rich segment experience with other searchresults on the search result web page if the search query received fromthe unique identity exceeds a relaxed-trigger threshold, presentingother search results on the search result web page without the richsegment experience if the search query received from the unique identitydoes not exceed the relaxed-trigger threshold, responsive to receivingthe search query from a different unique identity not previouslyassociated with the rich segment experience, presenting the rich segmentexperience with other search results if the search query received fromthe different unique identity exceeds a stringent-trigger thresholdgreater than the relaxed-trigger threshold, and presenting other searchresults on the search result web page without the rich segmentexperience if the search query received from the different uniqueidentity does not exceed the stringent-trigger threshold. In thisexample and/or other examples, logging the browsing history for theunique identity may include generating keyword tags derived from one ormore of previous search queries, previously selected search results,previously presented rich segment experiences. In this example and/orother examples, each rich segment experience may be defined in terms ofa different set of keyword tags, and the correlation criteria may besatisfied based on a set of keyword tags associated with the richsegment experience matching keyword tags associated with the uniqueidentity. In this example and/or other examples, logging the browsinghistory for the unique identity may include filtering out previouslyselected search results having dwell times that are less than athreshold time from being used to generate keyword tags for the uniqueidentity.

It will be understood that the configurations and/or approachesdescribed herein are exemplary in nature, and that these specificembodiments or examples are not to be considered in a limiting sense,because numerous variations are possible. The specific routines ormethods described herein may represent one or more of any number ofprocessing strategies. As such, various acts illustrated and/ordescribed may be performed in the sequence illustrated and/or described,in other sequences, in parallel, or omitted. Likewise, the order of theabove-described processes may be changed.

The subject matter of the present disclosure includes all novel andnon-obvious combinations and sub-combinations of the various processes,systems and configurations, and other features, functions, acts, and/orproperties disclosed herein, as well as any and all equivalents thereof.

What is claimed is:
 1. A method for presenting rich segment experienceson a search engine results page (SERP), the method comprising:receiving, at a computing system that is executing a search engine, aquery from a computing device that is in network communication with thecomputing system; identifying search results based upon the query;generating a first score for a first rich segment experience based uponthe query; generating a second score for a second rich segmentexperience based upon the query, wherein the first rich segmentexperience and the second rich segment experience are different from oneanother; generating the SERP based upon the query, the first score, andthe second score, wherein the SERP includes the search results, thefirst rich segment experience, and the second rich segment experience;and transmitting the SERP to the computing device for presentment to auser of the computing device.
 2. The method of claim 1, wherein thefirst rich segment experience relates to a first topic, and furtherwherein the second rich segment experience relates to a second topicthat is different from the first topic.
 3. The method of claim 1,wherein the first rich segment experience relates to a first subject,and further wherein the second rich segment experience relates to asecond subject that is different from the first subject.
 4. The methodof claim 1, wherein the first rich segment experience relates to a firsttheme, and further wherein the second rich segment experience relates toa second theme that is different from the first theme.
 5. The method ofclaim 1, wherein the first score is based further upon the searchresults and the second score is based further upon the search results.6. The method of claim 1, further comprising: determining that the firstscore is greater than a first predefined threshold value; determiningthat the second score is greater than a second predefined thresholdvalue that is different from the first predefined threshold value;selecting the first rich segment experience for inclusion in the SERPbased upon the first score being greater than the first predefinedthreshold value; and selecting the second rich segment experience forinclusion in the SERP based upon the second score being greater than thesecond predefined threshold value.
 7. The method of claim 6, wherein thefirst rich segment experience is assigned to a unique identity of a userwho submitted the query, and further wherein the first predefinedthreshold value is based upon the first rich segment experience beingassigned to the unique identity.
 8. The method of claim 7, wherein thesecond rich segment experience is not assigned to the unique identity,and further wherein the second predefined threshold value is higher thanthe first predefined threshold value due to the first rich segmentexperience being assigned to the unique identity while the second richsegment experience is not assigned to the unique identity.
 9. The methodof claim 1, wherein the first rich segment experience is assigned to aunique identity of a user who submitted the query, and further whereinthe first score is based upon the first rich segment experience beingassigned to the unique identity.
 10. The method of claim 10, wherein thesecond rich segment experience is not assigned to the unique identity,and further wherein the second score is based upon the second richsegment experience not being assigned to the unique identity.
 11. Acomputing system comprising: a processor; and memory storinginstructions that, when executed by the processor, cause the processorto perform acts comprising: receiving, from a client computing devicethat is in network communication with the computing system, a query setforth by a user of the client computing device; identifying searchresults based upon the query; identifying a first rich segmentexperience and a second rich segment experience based upon the query,wherein the first rich segment experience is related to a first topic,and further wherein the second rich segment experience is related to asecond topic that is different from the first topic; computing a firstscore for the first rich segment experience based upon the query;computing a second score for the second rich segment experience basedupon the query; selecting both the first rich segment experience and thesecond rich segment experience for inclusion in a search engine resultspage (SERP) based upon the first score and the second score,respectively; generating the SERP, wherein the SERP includes the searchresults, the first rich segment experience, and the second rich segmentexperience; and transmitting the SERP to the client computing device forpresentment to the user.
 12. The computing system of claim 11, the actsfurther comprising: performing a first comparison between the firstscore and a stringent-trigger threshold, wherein the first rich segmentexperience is selected for inclusion in the SERP based upon the firstcomparison; and performing a second comparison between the first scoreand a relaxed-trigger threshold that is different from thestringent-trigger threshold, wherein the second rich segment experienceis selected for inclusion in the SERP based upon the second comparison.13. The computing system of claim 12, wherein the user is associatedwith a unique identity, the unique identity has the second rich segmentexperience assigned thereto, and further wherein the relaxed-triggerthreshold is used in the second comparison due to the unique identityhaving the second rich segment experience assigned thereto.
 14. Thecomputing system of claim 13, wherein the unique identity does not havethe first rich segment experience assigned thereto, and further whereinthe stringent-trigger threshold is used in the first comparison due tothe unique identity not having the first rich segment experienceassigned thereto.
 15. The computing system of claim 14, wherein thestringent-trigger threshold is higher than the relaxed-triggerthreshold.
 16. The computing system of claim 12, wherein the uniqueidentity has the second rich segment experience assigned thereto basedupon an amount of overlap between a first set of keyword tags assignedto the unique identity and a second set of keyword tags assigned to thesecond rich segment experience.
 17. The computing system of claim 11,the acts further comprising: receiving, from a second client computingdevice that is in network communication with the computing system, thequery, wherein the query is set forth by a second user of the secondclient computing device; generating a second SERP based upon the query,wherein the second SERP fails to include the second rich segmentexperience; and transmitting the second SERP to the second clientcomputing device for display to the second user.
 18. The computingsystem of claim 11, wherein at least one of the first rich segmentexperience or the second rich segment experience includes a video.
 19. Anon-transitory computer-readable medium comprising instructions that,when executed by a processor, cause the processor to perform actscomprising: receiving, at a computing system that is executing a searchengine, a query from a computing device that is in network communicationwith the computing system; identifying search results based upon thequery; generating a first score for a first rich segment experiencebased upon the query; generating a second score for a second richsegment experience based upon the query, wherein the first rich segmentexperience is related to a first topic and the second rich segmentexperience is related to a second topic that is different from the firsttopic; selecting the first rich segment experience for inclusion in asearch engine results page (SERP) based upon the first score; selectingthe second rich segment experience for inclusion in the SERP based uponthe second score; generating the SERP, wherein the SERP includes thesearch results, the selected first rich segment experience, and theselected second rich segment experience; and transmitting the SERP tothe computing device for presentment to a user of the computing device.20. The non-transitory computer-readable medium of claim 19, the actsfurther comprising: receiving, at the computing system, the query from asecond computing device that is in network communication with thecomputing system; and generating a second SERP in response to receivingthe query, wherein the second SERP comprises the search results butfails to include at least one of the first rich segment experience orthe second rich segment experience.